The Efficiency Paradox and How to Save Yourself and the World

Inefficiency is ruining our planet and our lives. Efficiency is ruining our happiness, and weirdly, it’s also ruining our efficiency. Heeeeelppp!? What’s a techie to do? Zombie servers and slow code are a big climate problem, but the vrrrooom model gives us double-win hope. Holly walks through a range of techniques that can be used to find and eliminate software waste and reduce climate impacts.

But machine efficiency isn’t much use without human efficiency. Surprisingly, sorting out machine efficiency often helps humans, too. For example, the Quarkus Java framework uses many interesting waste-reduction techniques. These optimisations have the dual benefit of speeding up computers, and also speeding up people.

So far, so good, but we need to be careful we don’t end up accidentally optimising the wrong things, and making stuff worse. 100% utilisation is not sustainable for either humans, or people. It’s not even very efficient (what!?). Holly gives a brief tour of the brain’s default mode network, the latest business research, and queueing theory, to show why we actually achieve more when we do a bit less.

Transcript

1. The Efficiency Paradox and How to Save Yourself and the World Holly Cummins GOTO Copenhagen
2. @holly_cummins #RedHat now senior principal software engineer helping to build Quarkus
3. @holly_cummins #RedHat now senior principal software engineer helping to build Quarkus 2007 let’s make garbage collection more efficient!
4. @holly_cummins #RedHat now senior principal software engineer helping to build Quarkus 2007 let’s make garbage collection more efficient! 2015 lean and xp makes your team more efficient!
5. @holly_cummins #RedHat now senior principal software engineer helping to build Quarkus 2007 let’s make garbage collection more efficient! 2015 lean and xp makes your team more efficient! 2022 quarkus is wonderfully efficient!
6. @holly_cummins #RedHat “this provisioning software is broken”
7. @holly_cummins #RedHat what we sold “this provisioning software is broken” 10 minute provision-time
8. @holly_cummins #RedHat what we sold “this provisioning software is broken” 10 minute provision-time 3 month provision- time what the client thought they’d got
9. @holly_cummins #RedHat what we sold “this provisioning software is broken” 10 minute provision-time 3 month provision- time what the client thought they’d got the reason 84-step pre-approval process
10. @holly_cummins #RedHat waste
11. @holly_cummins #RedHat waste is killing the planet
12. @holly_cummins #RedHat 1700s machine
13. @holly_cummins #RedHat 1700s machine energy
14. @holly_cummins #RedHat 1700s machine energy energy
15. @holly_cummins #RedHat 1700s machine energy energy useful
16. @holly_cummins #RedHat 1700s waste machine energy energy useful
17. @holly_cummins #RedHat 1890s processes
18. @holly_cummins #RedHat time money 1890s processes
19. @holly_cummins #RedHat time money 1890s value processes
20. @holly_cummins #RedHat time money 1890s value processes
21. @holly_cummins #RedHat time money 1890s value processes waste
22. @holly_cummins #RedHat manufacturing 1900s
23. @holly_cummins #RedHat manufacturing 1900s value value
24. @holly_cummins #RedHat manufacturing 1900s value value value
25. @holly_cummins #RedHat manufacturing 1900s value value value waste
26. @holly_cummins #RedHat software 1960s
27. @holly_cummins #RedHat software time electricity hardware 1960s
28. @holly_cummins #RedHat software time electricity hardware answers 1960s
29. @holly_cummins #RedHat software time electricity hardware answers 1960s waste
30. @holly_cummins #RedHat waste is killing the planet
31. @holly_cummins #RedHat e-waste is killing the planet
32. @holly_cummins #RedHat energy waste is killing the planet
33. @holly_cummins #RedHat slow code is killing the planet
34. @holly_cummins #RedHat https://blog.linkedin.com/2017/august/3/making-linkedin-more-accessible-via-linkedin-lite
35. @holly_cummins #RedHat https://blog.linkedin.com/2017/august/3/making-linkedin-more-accessible-via-linkedin-lite “my heart sank … our new feature failed to load because of poor internet connectivity”
36. @holly_cummins #RedHat https://blog.linkedin.com/2017/august/3/making-linkedin-more-accessible-via-linkedin-lite modern web is so inefficient it is useless for part of its audience “my heart sank … our new feature failed to load because of poor internet connectivity”
37. @holly_cummins #RedHat forgotten code is killing the planet
38. #RedHat @holly_cummins@hachyderm.io 25% of 16,000 servers doing no useful work https://www.anthesisgroup.com/wp-content/uploads/2019/11/Comatose-Servers-Redux-2017.pdf zombie servers
39. #RedHat @holly_cummins@hachyderm.io the average server: 12 - 18% of capacity https://www.nrdc.org/sites/default/files/data-center-efficiency-assessment-IB.pdf
40. @holly_cummins #RedHat unused data is killing the planet
41. #RedHat @holly_cummins@hachyderm.io 68% of data is stored and never used https://www.nrdc.org/sites/default/files/data-center-efficiency-assessment-IB.pdf https://www.seagate.com/gb/en/news/news-archive/seagates-rethink-data-report-reveals-that-68-percent-of-data-available-to-businesses-goes-unleveraged-pr-master/
42. @holly_cummins #RedHat it’s not just money
43. @holly_cummins #RedHat it’s not just electricity it’s not just money
44. @holly_cummins #RedHat it’s not just electricity it’s not just money it’s embodied carbon
45. @holly_cummins #RedHat it’s not just electricity it’s water it’s not just money it’s embodied carbon
46. @holly_cummins #RedHat it’s not just electricity it’s water it’s e-waste it’s not just money it’s embodied carbon
47. @holly_cummins #RedHat we have solutions good news, everyone
48. @holly_cummins #RedHat making turning servers off as safe and easy as turning lights off. and then automate it.
49. @holly_cummins #RedHat solution: LightSwitchOps making turning servers off as safe and easy as turning lights off. and then automate it.
50. @holly_cummins #RedHat absurdly simple scripts my shell script to power down machines overnight saved my school €12,000
51. @holly_cummins #RedHat simple automation we used to leave our applications running all the time @darkandnerdy, Chicago DevOpsDays
52. @holly_cummins #RedHat simple automation we used to leave our applications running all the time when we scripted turning them off at night, we reduced our cloud bill by 30% @darkandnerdy, Chicago DevOpsDays
53. @holly_cummins #RedHat fancier scripts … with a frontend and a backend
54. @holly_cummins #RedHat
55. @holly_cummins #RedHat saves energy
56. @holly_cummins #RedHat saves energy saves money
57. @holly_cummins #RedHat saves energy saves money bonus: tests disaster recovery
58. @holly_cummins #RedHat saves energy saves money bonus: tests disaster recovery
59. @holly_cummins #RedHat
60. @holly_cummins #RedHat solution: faster code
61. @holly_cummins the vrroooom model
62. @holly_cummins
63. @holly_cummins invented by Dr. Vroom (really!)
64. @holly_cummins naming is the hardest problem in computer science invented by Dr. Vroom (really!)
65. @holly_cummins naming is the hardest problem in computer science
66. @holly_cummins my vrroooom model
68. @holly_cummins #RedHat
69. @holly_cummins #RedHat these two columns are almost the same
70. @holly_cummins #RedHat Energy 1 10 100 Time 1 10 100 energy efficiency across programming languages Python Rust Java Go
71. @holly_cummins #RedHat Energy 1 10 100 Time 1 10 100 the trend line is more or less straight energy efficiency across programming languages Python Rust Java Go
73. case study Quarkus
74. @holly_cummins #RedHat Java application frameworks were optimised for dynamism
75. @holly_cummins #RedHat Java application frameworks were optimised for dynamism dynamism has a cost
76. @holly_cummins #RedHat cloud apps are immutable now
77. @holly_cummins #RedHat cloud apps are immutable now
78. @holly_cummins #RedHat a highly dynamic runtime in a container is pointless
79. @holly_cummins Java dynamism
80. @holly_cummins Java dynamism build time
81. @holly_cummins Java dynamism build time runtime
82. @holly_cummins Java dynamism build time runtime
83. @holly_cummins Java dynamism packaging (maven, gradle…) build time runtime
84. @holly_cummins Java dynamism build time runtime
85. @holly_cummins Java dynamism build time runtime
86. @holly_cummins Java dynamism </> build time runtime load and parse • config files • properties • yaml • xml • etc.
87. @holly_cummins Java dynamism </> build time runtime
88. @holly_cummins Java dynamism @ @ </> build time runtime • classpath scanning and annotation discovery • attempt to load class to enable/disable features
89. @holly_cummins Java dynamism @ @ </> build time runtime
90. @holly_cummins Java dynamism @ @ </> build time runtime build a metamodel of the world
91. @holly_cummins Java dynamism @ @ </> build time runtime
92. @holly_cummins Java dynamism @ @ </> build time runtime start • thread pools • I/O • etc.
93. @holly_cummins Java dynamism @ @ </> build time runtime ready to do work!
94. @holly_cummins what if we start the application more than once? @ @ </>
95. @holly_cummins what if we start the application more than once? @ @ </> @ @ </>
96. @holly_cummins what if we start the application more than once? @ @ </> @ @ </> @ @ </>
97. @holly_cummins what if we start the application more than once? @ @ </> @ @ </> @ @ </> @ @ </>
98. @holly_cummins what if we start the application more than once? @ @ </> @ @ </> @ @ </> @ @ </>
99. @holly_cummins what if we start the application more than once? @ @ </> @ @ </> @ @ </> @ @ </> so much work gets redone every time
100. @holly_cummins Hibernate speed example: JTA auto-wiring
101. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”);
102. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”);
103. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”);
104. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”);
105. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); …
106. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”); …
107. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”); Class.forName(“VeryNicheJTAImplementation”); …
108. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”); Class.forName(“VeryNicheJTAImplementation”); …
109. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”); Class.forName(“VeryNicheJTAImplementation”); … ~129 auto-wiring attempts
110. @holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”); Class.forName(“VeryNicheJTAImplementation”); … ~129 auto-wiring attempts every single start.
111. @holly_cummins it’s not just JTA this happens for lots of internal service bindings
112. @holly_cummins the true cost of loaded classes isn’t just memory + start time
113. @holly_cummins the true cost of loaded classes isn’t just memory + start time method dispatching:
114. @holly_cummins interface the true cost of loaded classes isn’t just memory + start time method dispatching:
115. @holly_cummins unused implementation the one we want interface unused implementation unused implementation the true cost of loaded classes isn’t just memory + start time method dispatching:
116. @holly_cummins unused implementation the one we want interface unused implementation unused implementation the true cost of loaded classes isn’t just memory + start time method dispatching:
117. @holly_cummins unused implementation the one we want interface megamorphic call slow dispatching unused implementation unused implementation the true cost of loaded classes isn’t just memory + start time method dispatching:
118. @holly_cummins the true cost of loaded classes isn’t just memory + start time the one we want interface
119. @holly_cummins the true cost of loaded classes isn’t just memory + start time the one we want monomorphic call fast dispatching interface
120. @holly_cummins how do we fix all this?
121. @holly_cummins @ @ </> build time runtime what if we initialize at build time?
122. @holly_cummins @ @ </> build time runtime what if we initialize at build time?
123. @holly_cummins @ @ </> build time runtime start • thread pools • I/O • etc. what if we initialize at build time?
124. @holly_cummins @ @ </> build time runtime ready to do work! start • thread pools • I/O • etc. what if we initialize at build time?
125. @holly_cummins @ @ </> repeated starts are now efficient
126. @holly_cummins @ @ </> repeated starts are now efficient
127. @holly_cummins @ @ </> repeated starts are now efficient
128. @holly_cummins @ @ </> repeated starts are now efficient
129. @holly_cummins @ @ </> repeated starts are now efficient
130. @holly_cummins @ @ </> repeated starts are now efficient
131. @holly_cummins @ @ </> repeated starts are now efficient less wasted work
132. @holly_cummins #RedHat Setup: • REST + CRUD • large heap • RAPL energy measurement • multiple instances to support high load
 Assumptions: • US energy mix Source: John O’Hara effect on energy consumption (RAPL measurements)
133. @holly_cummins #RedHat Setup: • REST + CRUD • large heap • RAPL energy measurement • multiple instances to support high load
 Assumptions: • US energy mix Source: John O’Hara effect on energy consumption (RAPL measurements) vrrooom model in action quarkus on JVM has the lowest carbon … because it has the highest throughput
134. @holly_cummins throughput startup time + footprint
135. @holly_cummins we beat the trade-off. throughput startup time + footprint
136. @holly_cummins we beat the trade-off. throughput startup time + footprint
137. @holly_cummins we beat the trade-off. throughput startup time + footprint it’s a double-win.
139. @holly_cummins we beat another trade-off. human efficiency machine efficiency
140. @holly_cummins we beat another trade-off. human efficiency machine efficiency
141. @holly_cummins we beat another trade-off. human efficiency machine efficiency another double-win.
142. @holly_cummins #RedHat boilerplate code
143. @holly_cummins #RedHat boilerplate code
144. @holly_cummins #RedHat package com.example; import org.jboss.logging.Logger; public class Thing { private static final Logger log = Logger.getLogger(Thing.class); public void doSomething() { log.info("It works!"); } } example: logging
145. @holly_cummins #RedHat package com.example; import org.jboss.logging.Logger; public class Thing { private static final Logger log = Logger.getLogger(Thing.class); public void doSomething() { log.info("It works!"); } } example: logging import io.quarkus.logging.Log; Log
146. @holly_cummins #RedHat @ApplicationScoped public class GreetingRepository { public Entity findByName(int name) { return find("name", name).firstResult(); } void persist(Entity entity) {} void delete(Entity entity) {} Entity findById(Id id) {} List<Entity> list(String query, Sort sort, Object... params) { return null; } Stream<Entity> stream(String query, Object... params) { return null; } long count() { return 0; } long count(String query, Object... params) { return 0; } } example: hibernate with panache
147. @holly_cummins #RedHat @ApplicationScoped public class GreetingRepository { public Entity findByName(int name) { return find("name", name).firstResult(); } void persist(Entity entity) {} void delete(Entity entity) {} Entity findById(Id id) {} List<Entity> list(String query, Sort sort, Object... params) { return null; } Stream<Entity> stream(String query, Object... params) { return null; } long count() { return 0; } long count(String query, Object... params) { return 0; } } example: hibernate with panache
148. @holly_cummins #RedHat DAO example: hibernate with panache @ApplicationScoped public class GreetingRepository implements PanacheRepository<Greeting> { public Entity findByName(int name) { return find("name", name).firstResult(); } }
149. @holly_cummins #RedHat developer joy
150. @holly_cummins #RedHat no regrets solution: we got rid of dynamism we didn’t even want, and made everything better
151. @holly_cummins #RedHat the ai-lephant in the room
152. @holly_cummins #RedHat https://www.sify.com/ai-analytics/the-hilarious-and-horrifying-hallucinations-of-ai/
153. @holly_cummins #RedHat “What is the world record for crossing the English channel entirely on foot?” https://www.sify.com/ai-analytics/the-hilarious-and-horrifying-hallucinations-of-ai/
154. @holly_cummins #RedHat “What is the world record for crossing the English channel entirely on foot?” “The world record for crossing the English Channel entirely on foot is held by Christof Wandratsch of Germany, who completed the crossing in 14 hours and 51 minutes on August 14, 2020.” https://www.sify.com/ai-analytics/the-hilarious-and-horrifying-hallucinations-of-ai/
155. @holly_cummins #RedHat
156. @holly_cummins #RedHat
157. @holly_cummins #RedHat
158. @holly_cummins #RedHat
159. @holly_cummins #RedHat
160. @holly_cummins #RedHat
161. @holly_cummins #RedHat 70% unnecessary code
162. @holly_cummins #RedHat “illusion of efficiency”
163. @holly_cummins #RedHat we have a solution
164. @holly_cummins #RedHat -small fine-tuned models -small model + RAG -hybrid: symbolic reasoning + model solution:
165. @holly_cummins #RedHat illusions of efficiency are everywhere
166. @holly_cummins #RedHat
167. @holly_cummins #RedHat
168. @holly_cummins #RedHat how efficient can we get?
169. @holly_cummins #RedHat Jevon’s paradox limit 1
170. @holly_cummins #RedHat what we imagine when we widen roads
171. @holly_cummins #RedHat what we get
172. @holly_cummins #RedHat limit 2
173. @holly_cummins #RedHat even for machines, there is a limit to efficiency limit 2
174. @holly_cummins #RedHat even for machines, there is a limit to efficiency …and it’s lower than you think limit 2
175. @holly_cummins #RedHat source: http://geosci.uchicago.edu/~moyer/GEOS24705/2016/Assignments/PS7.pdf physics limit 2
176. @holly_cummins #RedHat limit 2
177. @holly_cummins #RedHat limit 2
178. @holly_cummins #RedHat theoretical max efficiency of a combustion engine: limit 2
179. @holly_cummins #RedHat theoretical max efficiency of a combustion engine: 37% limit 2
180. @holly_cummins #RedHat theoretical max efficiency of a combustion engine: 37% … so the engine doesn’t wear out: limit 2
181. @holly_cummins #RedHat theoretical max efficiency of a combustion engine: 37% … so the engine doesn’t wear out: ~20% limit 2
182. @holly_cummins #RedHat the value of slack
183. @holly_cummins #RedHat the value of slack
184. @holly_cummins #RedHat the value of slack
185. @holly_cummins #RedHat the value of slack naming is still the hardest problem in computer science
186. @holly_cummins #RedHat the value of slack
187. @holly_cummins #RedHat
188. @holly_cummins #RedHat speed
189. @holly_cummins #RedHat speed vs resiliency
190. @holly_cummins #RedHat #RedHat
191. @holly_cummins #RedHat less efficient (more legs than needed) #RedHat
192. @holly_cummins #RedHat highly efficient (optimum number of legs) less efficient (more legs than needed) #RedHat
193. @holly_cummins #RedHat highly efficient (optimum number of legs) less efficient (more legs than needed) resilient #RedHat
194. @holly_cummins #RedHat highly efficient (optimum number of legs) less efficient (more legs than needed) resilient #RedHat
195. @holly_cummins #RedHat highly efficient (optimum number of legs) no resiliency less efficient (more legs than needed) resilient #RedHat
196. @holly_cummins #RedHat highly efficient (optimum number of legs) no resiliency less efficient (more legs than needed) resilient #RedHat resiliency lowers efficiency
197. @holly_cummins #RedHat it’s the same for people
198. @holly_cummins #RedHat it’s the same for people all work and no play is … not efficient
199. @holly_cummins #RedHat
200. @holly_cummins #RedHat hi colleague, could you ple-
201. @holly_cummins #RedHat hi colleague, could you ple- argh! &!*$*%@!{*%^!^! busy! NO! I CANNOT!
202. @holly_cummins #RedHat research shows: not-work is good for business.
203. @holly_cummins #RedHat research shows: not-work is good for business.
204. @holly_cummins #RedHat studies show…
205. @holly_cummins #RedHat studies show… employee slack
206. @holly_cummins #RedHat studies show… employee slack
207. @holly_cummins #RedHat studies show… employee slack harder working
208. @holly_cummins #RedHat studies show… employee slack harder working more productivity
209. @holly_cummins #RedHat studies show… employee slack harder working more productivity less sick leave
210. @holly_cummins #RedHat https:/ /hbr.org/2012/01/positive-intelligence
211. @holly_cummins #RedHat “Your brain at positive is 31% more productive than your brain at negative, neutral or stressed. " https:/ /hbr.org/2012/01/positive-intelligence
212. #RedHat @holly_cummins
213. @holly_cummins #RedHat https:/ /wrap.warwick.ac.uk/63228/7/WRAP_Oswald_681096.pdf
214. @holly_cummins #RedHat "Individuals [who just watched a comedy video] have approximately 12% greater productivity." https:/ /wrap.warwick.ac.uk/63228/7/WRAP_Oswald_681096.pdf
215. @holly_cummins #RedHat play
216. @holly_cummins #RedHat play efficiency
217. @holly_cummins #RedHat doing nothing
218. @holly_cummins #RedHat doing nothing efficiency
219. @holly_cummins #RedHat the default mode network idle minds can solve hard problems
220. #RedHat @holly_cummins
221. #RedHat @holly_cummins
222. #RedHat @holly_cummins
223. #RedHat @holly_cummins 14% took showers specifically for the purpose of coming up with ideas
224. @holly_cummins #RedHat psychology says we need idle time; maths agrees
225. @holly_cummins #RedHat queueing theory says systems need to run under-capacity to function psychology says we need idle time; maths agrees
226. @holly_cummins #RedHat queuing theory basics
227. @holly_cummins #RedHat queuing theory basics arrival process
228. @holly_cummins #RedHat queuing theory basics arrival process Poisson distribution
229. @holly_cummins #RedHat queuing theory basics queue arrival process Poisson distribution
230. @holly_cummins #RedHat queuing theory basics queue arrival process Poisson distribution
231. @holly_cummins #RedHat queuing theory basics queue arrival process servers Poisson distribution
232. @holly_cummins #RedHat queuing theory basics queue arrival process servers Poisson distribution
233. @holly_cummins #RedHat queuing theory basics queue arrival process servers completed work Poisson distribution
234. @holly_cummins #RedHat if arrival rates are low, servers will be idle queue arrival process servers completed work
235. @holly_cummins #RedHat queue arrival process servers completed work if server capacity is too low, wait times are high
236. @holly_cummins #RedHat utilisation lead time http://brodzinski.com/2015/01/slack-time-value.html assuming Poisson distribution of arrivals
237. @holly_cummins #RedHat utilisation lead time http://brodzinski.com/2015/01/slack-time-value.html assuming Poisson distribution of arrivals 80% utilisation → 90% utilisation: wait times double
238. @holly_cummins #RedHat utilisation http://brodzinski.com/2015/01/slack-time-value.html cost delay cost assuming Poisson distribution of arrivals 80% utilisation → 90% utilisation: wait times double
239. @holly_cummins #RedHat utilisation http://brodzinski.com/2015/01/slack-time-value.html cost delay cost cost of idle capacity assuming Poisson distribution of arrivals 80% utilisation → 90% utilisation: wait times double
240. @holly_cummins #RedHat
241. @holly_cummins #RedHat cool and bouncy
242. @holly_cummins #RedHat cool and bouncy most efficient land animal
243. @holly_cummins #RedHat cool and bouncy most efficient land animal uncool and floppy
244. @holly_cummins #RedHat cool and bouncy most efficient land animal uncool and floppy whole body is basically slack
245. @holly_cummins #RedHat cool and bouncy most efficient land animal uncool and floppy whole body is basically slack most efficient animal
246. @holly_cummins the trade-off isn’t what we think it is being effective working less
247. @holly_cummins the trade-off isn’t what we think it is being effective working less
248. @holly_cummins the trade-off isn’t what we think it is being effective working less another double-win.
249. @holly_cummins #RedHat inverse Jevon’s paradox doing less achieves more
250. @holly_cummins #RedHat hygge
251. @holly_cummins #RedHat hygge “lazy cosy developers”
252. @holly_cummins #RedHat tl;dpa (too long; didn’t pay attention)
253. @holly_cummins #RedHat tl;dpa ⁃ don’t accept waste; our world deserves better (too long; didn’t pay attention)
254. @holly_cummins #RedHat tl;dpa ⁃ don’t accept waste; our world deserves better ⁃ work less; achieve more (too long; didn’t pay attention)
255. @holly_cummins #RedHat tl;dpa ⁃ don’t accept waste; our world deserves better ⁃ work less; achieve more ⁃ happiness is not waste (too long; didn’t pay attention)
256. @holly_cummins #RedHat tl;dpa ⁃ don’t accept waste; our world deserves better ⁃ work less; achieve more ⁃ happiness is not waste ⁃ idleness is not waste (too long; didn’t pay attention)
257. @holly_cummins #RedHat tl;dpa ⁃ don’t accept waste; our world deserves better ⁃ work less; achieve more ⁃ happiness is not waste ⁃ idleness is not waste ⁃ look for double-wins everywhere (too long; didn’t pay attention)
258. Rate this session in the GOTO Guide and claim your reward slides https://hollycummins.com/efficiency-goto/